docs(samples): Update BigQuery Storage Arrow samples batching logic

packages/google-cloud-bigquery-storage/samples/pyarrow/append_rows_with_arrow.py

@@ -16,14 +16,14 @@
 import datetime
 import decimal
 
-from google.cloud import bigquery
 from google.cloud.bigquery import enums
-from google.cloud.bigquery_storage_v1 import types as gapic_types
-from google.cloud.bigquery_storage_v1.writer import AppendRowsStream
 import pandas as pd
-
 import pyarrow as pa
 
+from google.cloud import bigquery
+from google.cloud.bigquery_storage_v1 import types as gapic_types
+from google.cloud.bigquery_storage_v1.writer import AppendRowsStream
+
 TABLE_LENGTH = 100_000
 
 BQ_SCHEMA = [
@@ -100,7 +100,10 @@ def make_table(project_id, dataset_id, bq_client):
 
 
 def create_stream(bqstorage_write_client, table):
-    stream_name = f"projects/{table.project}/datasets/{table.dataset_id}/tables/{table.table_id}/_default"
+    stream_name = (
+        f"projects/{table.project}/datasets/{table.dataset_id}/"
+        f"tables/{table.table_id}/_default"
+    )
     request_template = gapic_types.AppendRowsRequest()
     request_template.write_stream = stream_name
 
@@ -160,18 +163,64 @@ def generate_pyarrow_table(num_rows=TABLE_LENGTH):
 
 
 def generate_write_requests(pyarrow_table):
-    # Determine max_chunksize of the record batches. Because max size of
-    # AppendRowsRequest is 10 MB, we need to split the table if it's too big.
-    # See: https://cloud.google.com/bigquery/docs/reference/storage/rpc/google.cloud.bigquery.storage.v1#appendrowsrequest
-    max_request_bytes = 10 * 2**20  # 10 MB
-    chunk_num = int(pyarrow_table.nbytes / max_request_bytes) + 1
-    chunk_size = int(pyarrow_table.num_rows / chunk_num)
-
-    # Construct request(s).
-    for batch in pyarrow_table.to_batches(max_chunksize=chunk_size):
+    # Maximum size for a single AppendRowsRequest is 10 MB.
+    # To be safe, we'll aim for a soft limit of 7 MB.
+    max_request_bytes = 7 * 1024 * 1024  # 7 MB
+
+    def _create_request(batches):
+        """Helper to create an AppendRowsRequest from a list of batches."""
+        combined_table = pa.Table.from_batches(batches)
         request = gapic_types.AppendRowsRequest()
-        request.arrow_rows.rows.serialized_record_batch = batch.serialize().to_pybytes()
-        yield request
+        request.arrow_rows.rows.serialized_record_batch = (
+            combined_table.combine_chunks().to_batches()[0].serialize().to_pybytes()
+        )
+        return request
+
+    batches = pyarrow_table.to_batches()
+
+    current_batches = []
+    current_size = 0
+
+    while batches:
+        batch = batches.pop()
+        batch_size = batch.nbytes
+
+        if current_size + batch_size > max_request_bytes:
+            if batch.num_rows > 1:
+                # Split the batch into 2 sub batches with identical chunksizes
+                mid = batch.num_rows // 2
+                batch_left = batch.slice(offset=0, length=mid)
+                batch_right = batch.slice(offset=mid)
+
+                # Append the new batches into the stack and continue poping.
+                batches.append(batch_right)
+                batches.append(batch_left)
+                continue
+
+            # If the batch is single row and still larger than max_request_size
+            else:
+                # If current batches is empty, throw error
+                if len(current_batches) == 0:
+                    raise ValueError(
+                        f"A single PyArrow batch of one row is larger than the maximum request size "
+                        f"(batch size: {batch_size} > max request size: {max_request_bytes}). Cannot proceed."
+                    )
+                # Otherwise, generate the request, reset current_size and current_batches
+                else:
+                    yield _create_request(current_batches)
+
+                    current_batches = []
+                    current_size = 0
+                    batches.append(batch)
+
+        # Otherwise, add the batch into current_batches
+        else:
+            current_batches.append(batch)
+            current_size += batch_size
+
+    # Flush remaining batches
+    if current_batches:
+        yield _create_request(current_batches)
 
 
 def verify_result(client, table, futures):
@@ -181,14 +230,13 @@ def verify_result(client, table, futures):
     assert bq_table.schema == BQ_SCHEMA
 
     # Verify table size.
-    query = client.query(f"SELECT COUNT(1) FROM `{bq_table}`;")
+    query = client.query(f"SELECT DISTINCT int64_col FROM `{bq_table}`;")
     query_result = query.result().to_dataframe()
 
-    # There might be extra rows due to retries.
-    assert query_result.iloc[0, 0] >= TABLE_LENGTH
+    assert len(query_result) == TABLE_LENGTH
 
     # Verify that table was split into multiple requests.
-    assert len(futures) == 2
+    assert len(futures) == 3
 
 
 def main(project_id, dataset):